Using implied volatility on options to measure the relation between asset returns and variability

Macroeconomic are important variables influencing volatility in the bond market. Some of the challenges faced such as default risk, liquidity risk, interest rate risk, inflation risk, and exchange rate risk. This study is aimed at examining asymmetric volatility using the EGARCH model and at estimating macroeconomic variables which influence the return of Indonesian Government bonds. The asymmetric volatility can be measured by determining the best order value of the EGARCH model. Based on the findings of the study, EGARCH (2.1) is the best model for assessing volatility in short-term SUN returns, EGARCH (3.1) for medium-term SUN and EGARCH (2.3) for the long term. The asymmetric volatility pertains in the returns of short, medium and long-term government bonds. In addition, the negative information has a greater impact than positive information in the short, medium and long term. The deposit rate and return of the Composite Stock Price Index have a significant positive effect on short, medium and long term bond returns. The effective federal funds rate or FED interest rate has a significant positive effect on the return of short and long-term SUN bonds while in the medium term it has no effect. Exchange rates have a significant negative effect on short, medium and long term bond returns.

Independent Auditor’s Report

This dissertation is composed of three loosely related chapters, all of which are empirical.In Chapter 1, I examine whether expectations are formed in a systematically different manner during periods of low volatility versus periods of high volatility. I achieve this by measuring non-linearities in relationship between the SP 500 and the VIX across different market regimes. Three distinct market regimes are identified through a Markov Process, allowing for the capture of non-constant behavior in the relationship between contemporaneous price changes and future volatility expectations. The results indicate that the effect of the underlying asset on the supply and demand dynamics of its derivative is strongest during periods of low volatility and weakest during periods of high volatility. The decrease in magnitude of the SP 500 coefficient as the market switches from low volatility to high, suggests that information scarcity (low volatility) makes additional data (price changes) more impactful. Measures to limit market volatility may make market participant prone to expect changes in the state of the system. The purpose of Chapter 2 is to draw inference from the tail behavior of financial market price volatility in order to compare and contrast volatility expectations with volatility realizations. In doing so, I discuss the implications of slowly decaying tails as they relate to systems susceptible to unpredictable and consequential events. In such cases where fat tails are identified, typical values such as the average and variance, do not properly characterize the risk and unpredictability of the dynamic process under study. Prior research has identified asset prices and asset volatility as being drawn from a power law distribution. This paper aims to quantitatively confirm this characterization, specifically for market volatility. Further, this paper identifies whether or not volatility expectations exhibit similar power law characteristics. Goodness of fit and log likelihood tests indicate that most realized volatility series are plausibly drawn from a power- law distribution. However, none of the studied implied volatility series show evidence of power-law behavior, suggesting that risk premia may exist for lower levels of volatility but does not scale proportionally to the more extreme crisis events. That is, risk premia does not scale proportionally as values move farther into the tail. In Chapter 3, co-authored with Minh Pham, we investigate how economic uncertainty, specifically stock market uncertainty, correlates to individuals' life-satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life-satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life- satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicates that economic sentiment about the future plays an important role in individuals' feelings about the present. This work was inspired by a desire to understand the economic crises that redirect and ultimately redefine our socioeconomic lives, as individuals and as nations. I began my economic studies during one of the most profound crises in recent history, the global financial crisis of the late 2000s. Here again in 2021, as my studies conclude, economies grapple with another, albeit different crisis. Both the Covid-19 pandemic and the subprime financial crisis highlight a salient fact; we never really know when, why, or from where such extreme events arrive. But they do, and do so more frequently than we like or predict. Each of the chapters presented in this dissertation seek to understand the ways in which we anticipate and interact with a characteristic marker of economic and financial crises, uncertainty.

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.

Strategic Asset Allocation

Purpose of research. It is necessary to clearly define the composition and structure of economic assets to conduct a systematic statistical analysis of the state and development of the national economy, including at the level of sectors, subsectors and institutional units.The purpose of the work is to systematize data on the availability and movement of economic assets in the context of information sources, methods of their assessment and analysis of the practice of macroeconomic calculations.Materials and methods. In this paper, the authors considered the classification of economic assets, applied structural and dynamic data analysis, as well as methods of theoretical research in the form of generalization, comparison, and special analytical procedures.Results. Statistical study of economic transactions with assets involves a detailed review of the groupings and classifications of assets at the level of institutional units, sectors of the economy and the economy as a whole, which will provide reliable information about their availability and movement. In addition, it is necessary to understand the essence of statistical indicators that reflect the state and change in the value of assets, the methodology for their calculation, comprehensive analysis and practical use. The paper defines the main directions for studying economic assets based on current international standards, taking into account national statistical practice.Conclusion. As part of a systematic statistical analysis of the state and development of the national economy in various areas, it is necessary to clearly define the composition and structure of economic assets, their place and significance in the production of goods and services.The issue of assessing the value of economic assets, which should reflect their market value, is of significant importance. Improving methods for assessing economic assets is an urgent problem not only for macroeconomic calculations, but also for adequately reflecting economic operations at the level of institutional units and sectors of the economy.Economic assets are recognized as such if they bring economic benefits to their owner. Evaluating the effectiveness of asset use in economic activities or asset ownership is no less challenging for analysts. Such analytical procedures require a comprehensive analysis of data on the availability and movement of assets, their composition and structure, including from an international perspective.The authors of the article have repeatedly addressed the issues of macroeconomic calculations in their publications, which allowed them to consider in more detail topical issues related to the accumulation of non-financial and financial assets.

A rise in the yield of financial market assets could lead to variations in the returns of other assets over time due to arbitrage conditions. Consequently, this phenomenon may trigger spillover effects or cointegration among the volatilities of assets within financial markets. The aim of this study is to investigate spillover effects among American, European, Russian, and Turkish stock markets during the COVID-19 pandemic and the Russia-Ukraine war. Employing the diagonal BEKK-GARCH model from 2020 to 2023, the volatility transmissions within stock returns is examined. The results reveal significant GARCH effects alongside modest ARCH effects. Notably, during the COVID-19 period, the European market exerted the most significant influence on other markets, whereas during the war period, the US market dominated, and Turkish markets displaying the least impact for two periods. Furthermore, the findings indicate that the lagged cross-volatility persistence is lower during the Russia-Ukraine war period compared to the COVID-19 period.

Since June 2007, the creditworthiness of structured finance products has deteriorated rapidly. The number of downgrades in November 2007 alone exceeded 2,000 and many downgrades were severe, with 500 tranches downgraded more than 10 notches. Massive downgrades continued in 2008. More than 11,000 of the downgrades affected securities that were rated AAA. This paper studies the credit rating crisis of 2007-2008 and in particular describes the collapse of the credit ratings of ABS CDOs. Using data on ABS CDOs we provide suggestive evidence that ratings shopping may have played a role in the current crisis. We find that tranches rated solely by one agency, and by S&P in particular, were more likely to be downgraded by January 2008. Further, tranches rated solely by one agency are more likely to suffer more severe downgrades.(This abstract was borrowed from another version of this item.)

Globalization and Asset Returns

Many investors expect a well-crafted asset allocation strategy to provide exceptional risk management through diversification. However, research has shown that asset returns can be highly correlated during down markets and periods of extreme market turmoil, reducing the effectiveness of diversification. In <i><b>Macro Factor Investing with Style</b></i>, from the 2022 Quantitative Strategies special issue of <i><b>The Journal of Portfolio Management</b></i>, <b>Alexander Swade</b> (<b>Invesco</b>), <b>Harald Lohre</b> (<b>Invesco</b>), <b>Mark Shackleton</b> (<b>Lancaster University</b>), <b>Sandra Nolte</b> (<b>Lancaster University</b>), <b>Scott Hixon</b> (<b>Invesco</b>), and <b>Jay Raol</b> (<b>Invesco</b>), propose that investors diversify using macroeconomic factors instead of asset allocation. Macro factors have proven to be better at clarifying the variation of investment returns across asset classes and style factors in specific economic situations. The authors demonstrate that focusing on three relevant macro factors—growth, inflation, and defensive—enhances portfolio risk management by investing in macro factors that show high exposure to different market states.

Target date funds (TDFs) provide retirement investors, many of whom are unsophisticated or inattentive, with age-appropriate exposures to different asset classes like stocks and bonds. To maintain exposures, TDFs trade actively against market returns, buying stock funds when the stock market does poorly, and selling when the market does well (Parker et al., 2023, Journal of Finance). This paper shows that trading by TDFs was a significant stabilizing force in US equity markets during the unprecedented economic volatility of the COVID-19 pandemic period. Specifically, TDFs – now comprising a quarter of all 401(k) plan assets – caused significant contrarian investment flows across asset classes, flows that were not undone by enrollment of TDF investors or by discretionary actions by TDF managers. Mutual funds with large ownership by TDFs had more stable funding through the pandemic, and stocks that had greater indirect ownership by TDFs had lower co-movement with the market and lower volatility during the pandemic period.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

Market volatility across asset classes during U.S. presidential and mid-term elections

Portfolio managers have long sought the ability to increase diversification and hedge market downturns without sacrificing upside returns. Using volatility as a diversifying asset is an attractive proposition because of volatility’s asymmetric response to underlying price movements. Theoretically, being able to hold the CBOE Volatility Index in a portfolio should provide substantial benefits to a portfolio. The authors find, however, that currently available VIX-related products are costly in their implementation and yield negative abnormal returns. Even so, if investors insist on investing in volatility assets, using VIX futures offers the best Sharpe ratios.

Exchange-traded funds (ETFs) have emerged as a favored investment vehicle due to their liquidity, affordability, and diversified exposure across asset classes. However, their price dynamics, influenced by macroeconomic trends, market sentiment, and real-time news, challenge traditional forecasting methods. This study advances ETF price prediction by integrating the probabilistic forecasting with autoregressive recurrent networks (DeepAR) with normalized sentiment scores (NSS) and a novel temporal keyword-enhanced sentiment framework. Temporal keyword-enhanced sentiment combines trending keywords from Google Trends with user-defined macroeconomic terms to enrich news sentiment capture, while NSS consolidates conflicting sentiments into a daily signal. Applied to eight U.S. ETFs spanning bonds, equities, and commodities, the enhanced DeepAR-NSS model consistently outperformed the baseline DeepAR, reducing mean absolute percentage error by 22-49.7% and root mean square error by up to 40.6%. Notable improvements were observed in bond ETFs (e.g., GOVT) and volatile assets (e.g., GLD), though high-risk ETFs like JNK exhibited increased root mean square error variability. These findings highlight the efficacy of sentiment integration in financial forecasting and underscore the need for asset-specific model refinement.